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Dynamic Microgrid Formation Considering Time-dependent Contingency: A Distributionally Robust Approach

Ziang Liu, Sheng Cai, Qiuwei Wu, Xinwei Shen, Xuan Zhang, Nikos Hatziargyriou

TL;DR

The paper tackles resilience during long-duration extreme weather by dynamically forming microgrids that adapt to time-dependent line-failure probabilities. It introduces a distributionally robust DMF framework (DR-DMF) that minimizes the worst-case expected load shedding over contingencies, using a two-stage formulation with a time-aware ambiguity set. The model combines radial microgrid topology, LinDistFlow-based operation, and a robust optimization approach solved via column-and-constraint generation, validated on a modified IEEE 37-bus system. Results show DR-DMF substantially reduces weighted loss of load compared to static DMF and pure N-k robust methods, demonstrating practical resilience gains under typhoon-style disturbances.

Abstract

The increasing frequency of extreme weather events has posed significant risks to the operation of power grids. During long-duration extreme weather events, microgrid formation (MF) is an essential solution to enhance the resilience of the distribution systems by proactively partitioning the distribution system into several microgrids to mitigate the impact of contingencies. This paper proposes a distributionally robust dynamic microgrid formation (DR-DMF) approach to fully consider the temporal characteristics of line failure probability during long-duration extreme weather events like typhoons. The boundaries of each microgrid are dynamically adjusted to enhance the resilience of the system. Furthermore, the expected load shedding is minimized by a distributionally robust optimization model considering the uncertainty of line failure probability regarding the worst-case distribution of contingencies. The effectiveness of the proposed model is verified by numerical simulations on a modified IEEE 37-node system.

Dynamic Microgrid Formation Considering Time-dependent Contingency: A Distributionally Robust Approach

TL;DR

The paper tackles resilience during long-duration extreme weather by dynamically forming microgrids that adapt to time-dependent line-failure probabilities. It introduces a distributionally robust DMF framework (DR-DMF) that minimizes the worst-case expected load shedding over contingencies, using a two-stage formulation with a time-aware ambiguity set. The model combines radial microgrid topology, LinDistFlow-based operation, and a robust optimization approach solved via column-and-constraint generation, validated on a modified IEEE 37-bus system. Results show DR-DMF substantially reduces weighted loss of load compared to static DMF and pure N-k robust methods, demonstrating practical resilience gains under typhoon-style disturbances.

Abstract

The increasing frequency of extreme weather events has posed significant risks to the operation of power grids. During long-duration extreme weather events, microgrid formation (MF) is an essential solution to enhance the resilience of the distribution systems by proactively partitioning the distribution system into several microgrids to mitigate the impact of contingencies. This paper proposes a distributionally robust dynamic microgrid formation (DR-DMF) approach to fully consider the temporal characteristics of line failure probability during long-duration extreme weather events like typhoons. The boundaries of each microgrid are dynamically adjusted to enhance the resilience of the system. Furthermore, the expected load shedding is minimized by a distributionally robust optimization model considering the uncertainty of line failure probability regarding the worst-case distribution of contingencies. The effectiveness of the proposed model is verified by numerical simulations on a modified IEEE 37-node system.
Paper Structure (12 sections, 10 equations, 5 figures, 1 table)

This paper contains 12 sections, 10 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Model Formulation
  4. Objective function
  5. Constraints
  6. Microgrid topology constraints
  7. Post-events operation constraints
  8. Solution Methodology
  9. Numerical Simulations
  10. Optimization Results of the DR-DMF Model
  11. Monte Carlo Simulation and Comparisons
  12. Conclusion

Figures (5)

  • Figure 1: An illustrative example for dynamic microgrid formation
  • Figure 2: Typical response of a resilient system after a disruption
  • Figure 3: The modified IEEE 37-bus system
  • Figure 4: The result of the DR-DMF model in four time steps
  • Figure 5: Box-plot of weighted load shedding of three methods